The present invention relates to resource planning in a computerized environment and specifically to using inferential and artificially intelligent methodologies to predict and identify extrinsic events that will require provisioning of additional resources.
Determining how to allocate resources in infrastructure-managed environments (such as provisioning a virtual machine in a cloud-computing environment or redirecting storage or processing power to a particular node of an enterprise network) is typically performed as a function of expected utilization. For example, if it is known that a video-streaming service requires more bandwidth on weekends, a greater number of network resources may be allocated during those peak times.
It is not as easy, however, to predict less-obvious effects of irregular, extrinsic, or environmental factors, such as a catastrophic weather event, political news, sporting events, product announcements, or financial news. For example, a long-term outage at a power plant may greatly increase traffic at Web sites of hardware stores across the region or an announcement of a new single by a popular artist may increase the popularity of that artist's Facebook page, Twitter feed, eBay store, or personal Web site.
Current forecasting and allocation methods are especially challenged when resource demand is driven by an interaction among multiple factors. If, for example, i) a national holiday falls on an upcoming weekend, ii) a blizzard is expected to snarl automobile traffic in a local region during that weekend; and iii) a major train route runs through that region, then there might be a likelihood that traffic on the train carrier's ticketing Web site will increase during the week preceding the holiday. A failure to consider all these factors might result in a failure to add additional trains in time to handle the increased workload and sold-out reservations.
There is thus a need to identify, correlate, and predict the effect of extrinsic or environmental events upon utilization of computerized resources, and to do so early enough to enable the provisioning of additional required resources.